Commercialization of a Monitor to Assess Neurological Health in Neonates

描述（申请人提供）：与成人不同，新生儿无法接受传统的精神状态测试（定向力、语言、记忆力等）来衡量大脑皮层的功能然而，EEG的使用被公认为是表征新生儿大脑皮层健康状况的唯一半客观手段之一。不幸的是，专家新生儿脑电图解释的可用性是非常有限的，目前在该国相对较少的专业儿科中心。即使可用，新生儿EEG的审查和解释也是时间和劳动密集型的。由于这些限制因素，许多医院很少在新生儿重症监护室进行EEG监测，无论其在神经评估中的价值如何。该项目设想的设备是一种EEG监测仪，可以监测新生儿的神经健康，并能够由临床医生进行完整的EEG审查和验证。为该监护仪开发的算法专门分析新生儿EEG以明确定义异常，以便提供以下范围之一的简单神经学评估评分（N3指数）：（i）正常，（ii）轻度异常，（iii）中度异常，或（iv）严重异常。评估算法包括用于量化新生儿EEG中存在的任何不连续或不对称的幅度、持续时间和频率组成的信号处理技术。该算法还包括用于伪影效应最小化和前景事件与背景EEG的区分的方法。所选信号处理参数的数据融合分析计算最终评估分数，该分数随时间显示以产生EEG评估的可靠趋势。从所提出的监测器更准确地诊断神经系统高危新生儿可能有几个好处。首先，它们会影响护理决策的水平，特别是对于那些护理不佳的新生儿（例如：严重神经功能缺损或死亡）。其次，EEG背景的连续评估可以是治疗成功的量度，指示在已经给予治疗之后的神经改善或下降。拟议项目的相关性在于，开发一种易于使用的具有准确分析方法的新生儿脑电图监测仪，可以更广泛地监测新生儿的大脑状态。它还可以更早地检测和管理这一人群的脑损伤，这可以促进改善结果和降低医疗保健成本。

DESCRIPTION (provided by applicant): Unlike adults, neonates cannot undergo traditional mental status testing (orientation, language, memory, etc.) as a measure of cortical function. However, the use of EEG is well established as one of the only semi-objective means to characterize the health of the cerebral cortex in neonates. Unfortunately, the availability of expert neonatal EEG interpretation is very limited, and is present at relatively few specialized pediatric centers in the country. Even when available, the review and interpretation of neonatal EEG is time and labor intensive. Because of these limiting factors, many hospitals rarely perform EEG monitoring in the neonatal intensive care unit, regardless of its value in neurological assessment. The envisioned device from this project is an EEG monitor that can trend the neurological wellness of neonates, with the capability for complete EEG review and verification by clinicians. The algorithm being developed for this monitor specifically analyzes neonatal EEG for clearly defined abnormalities in order to provide a simple neurological assessment score (the N3 Index) in one of the following ranges: (i) normal, (ii) mildly abnormal, (iii) moderately abnormal, or (iv) severely abnormal. The assessment algorithm consists of signal processing techniques used to quantify the amplitudes, durations, and frequency compositions of any discontinuities or asymmetries present in neonatal EEGs. The algorithm also includes methods for artifact effect minimization and the discrimination of foreground events from the background EEG. A data fusion analysis of the selected signal processing parameters calculates the final assessment score, which is displayed over time to produce a reliable trend of EEG assessment. More accurate prognoses of neurologically at-risk newborns from the proposed monitor may have several benefits. First, they can impact level of care decisions, especially for those neonates given poor prognoses (e.g.: severe neurological deficits or death). Secondly, the continuous assessment of the EEG background can be a measure of treatment success, indicating neurological improvement or decline after treatment has been given. The relevance of the proposed project is that the development of an easy-to-use neonatal EEG monitor with accurate analysis methods could enable more widespread monitoring of brain status in newborns. It also would enable earlier detection and management of brain injury in this population, which could promote both improved outcomes and reduced health care costs.